How can I read any AI paper?
I think the answer depends very much on why you are reading the paper, what are you trying to get out of it? There are plenty of papers that I &quot;read&quot; (or often really just quickly skim through) where I'll definitely not understand all the math. More often than not, this will be because I don't actually care to deeply understand it.
There is plenty of more &quot;practical&quot; research to be done in AI, which definitely doesn't always require a deep understanding of all the math. Intuition can often be enough, at least to get started, for meaningful practical contributions. If this is the sort of research that you're interested in doing, you probably don't need to understand as many of the mathematical parts of AI papers as you do if you're really trying to do research directly in that theoretical area.
Personally, when I write &quot;math-heavy&quot; parts in my own papers (and that will often already be restricted to a rather simple level of math in comparison to the &quot;real theory&quot; ML papers), I always try to make sure to include intuitive, English descriptions of what we're doing around it. Even if you don't immediately understand a full equation, just having the intuitive explanation around it to tell you what it means can be enough for a broad understanding of the paper. Then you only have to dive deep into the details of the equations if -- based on the English text -- you decide that you're actually really interested. So, if there are sufficient, intuitive explanations surrounding the equations, I'd recommend to focus heavily on that first. Not every paper does this though, sometimes there's very little text and very much math, and then this can be difficult.
Even if it turns out that you do have to understand math, you may not have to understand ALL of it right away though. The important parts that I would try to focus on understanding first are:

A mathematical description of the &quot;problem&quot;. This could be an objective function, a metric to be optimised/minimised/maximised, or an existing equation from previous literature that the authors take as a starting point and inspect some detail of in greater detail.
Mathematical descriptions of the outcomes/results. These could be equations that they actually use in concrete algorithms (see if you can relate them to any pseudocode that may be present), or the final equations stated in theorems / at the end of proofs.

All the complex parts in between are probably less important. Just a vague idea of what the starting point is, and a vague understanding of the final outcome, can be enough to at least know what the paper is about. Then you can decide for yourself whether you really need to know more about the details in between, or if they're maybe not relevant to you / your work / your research.